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Dementia classification from speech using machine learning

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new method for classifying dementia using spontaneous speech, analyzing acoustic features from entire recordings rather than just speech-active segments. This approach, utilizing the openSMILE toolkit and wrapper-based feature selection, identifies diagnostically relevant characteristics. The extreme minimal learning machine classifier proved to be the most computationally efficient, offering competitive accuracy and serving as a supportive tool for dementia assessment. AI

IMPACT This research could lead to more accessible and efficient tools for early dementia detection, aiding clinical assessment.

RANK_REASON Academic paper detailing a novel methodology for dementia classification using machine learning on speech data. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

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Dementia classification from speech using machine learning

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Academic paper detailing a novel methodology for dementia classification using machine learning on speech data. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Marko Niemel\"a, Mikaela von Bonsdorff, Sami \"Ayr\"am\"o, Tommi K\"arkk\"ainen ·

    Dementia classification from spontaneous speech using wrapper-based feature selection

    arXiv:2502.03484v3 Announce Type: replace-cross Abstract: Dementia encompasses a group of syndromes that impair cognitive functions such as memory, reasoning, and the ability to perform daily activities. As populations globally age, nearly 10 million new dementia cases occur annu…

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